Mesh is an essential and effective data representation of a 3D shape. The 3D mesh segmentation is a fundamental task in computer vision and graphics. It has recently been realized through a multi-scale deep learning framework, whose sampling methods are of key significance. Rarely do the previous sampling methods consider the receptive field contour of vertex, leading to loss in scale consistency of the vertex feature. Meanwhile, uniform sampling can ensure the utmost uniformity of the vertex distribution of the sampled mesh. Consequently, to efficiently improve the scale consistency of vertex features, uniform sampling was first used in this study to construct a multi-scale mesh hierarchy. In order to address the issue on uniform sampling, namely, the smoothing effect, vertex clustering sampling was used because it can preserve the geometric structure, especially the edge information. With the merits of these two sampling methods combined, more and complete information on the 3D shape can be acquired. Moreover, we adopted the attention mechanism to better realize the cross-scale shape feature transfer. According to the attention mechanism, shape feature transfer between different scales can be realized by the construction of a novel graph structure. On this basis, we propose dual-sampling attention pooling for graph neural networks on 3D mesh. According to experiments on three datasets, the proposed methods are highly competitive.

网格是 3D 形状的重要且有效的数据表示。3D 网格分割是计算机视觉和图形中的一项基本任务。它最近通过多尺度深度学习框架实现，其采样方法具有关键意义。以前的采样方法很少考虑顶点的感受野轮廓，导致顶点特征的尺度一致性损失。同时，均匀采样可以保证采样网格顶点分布的最大均匀性。因此，为了有效提高顶点特征的尺度一致性，本研究首先使用均匀采样来构建多尺度网格层次结构。为了解决均匀采样的问题，即平滑效果，使用顶点聚类采样是因为它可以保留几何结构，尤其是边缘信息。结合这两种采样方法的优点，可以获得更多完整的 3D 形状信息。此外，我们采用了注意力机制来更好地实现跨尺度形状特征转移。根据注意力机制，可以通过构建新的图结构来实现不同尺度之间的形状特征转移。在此基础上，我们为 3D 网格上的图神经网络提出了双采样注意力池。根据在三个数据集上的实验，所提出的方法具有很强的竞争力。我们采用了注意力机制来更好地实现跨尺度的形状特征转移。根据注意力机制，可以通过构建新的图结构来实现不同尺度之间的形状特征转移。在此基础上，我们为 3D 网格上的图神经网络提出了双采样注意力池。根据在三个数据集上的实验，所提出的方法具有很强的竞争力。我们采用了注意力机制来更好地实现跨尺度的形状特征转移。根据注意力机制，可以通过构建新的图结构来实现不同尺度之间的形状特征转移。在此基础上，我们为 3D 网格上的图神经网络提出了双采样注意力池。根据在三个数据集上的实验，所提出的方法具有很强的竞争力。